{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "S+P Week 2 Exercise Answer.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1J15Vh_1Jih",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "92a5b5a2-3634-425c-c5e2-46692ff28973"
      },
      "source": [
        "!pip install tf-nightly-2.0-preview\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tf-nightly-2.0-preview\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b8/48/ba86399b1e11ec23bc3243de942c5bccd7f1471f392d5c5aea39033e7dc4/tf_nightly_2.0_preview-2.0.0.dev20190917-cp36-cp36m-manylinux2010_x86_64.whl (91.3MB)\n",
            "\u001b[K     |████████████████████████████████| 91.3MB 1.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (0.2.2)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (0.1.7)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (0.8.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (3.0.1)\n",
            "Collecting tb-nightly<2.1.0a0,>=2.0.0a0 (from tf-nightly-2.0-preview)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2c/1c/194dd57b4164caf8cf149843c6c240477bd7c8e91c9f7a842ea3f7f114b2/tb_nightly-2.0.0a20190917-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 30.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.16.5)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.11.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (0.33.6)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.1.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.0.8)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.1.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (0.8.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (3.7.1)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.15.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.12.0)\n",
            "Collecting tensorflow-estimator-2.0-preview (from tf-nightly-2.0-preview)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/20/97/d6cb14342da0206236e6a6da5e57cfb634c781d9c82dec1f7e319a72bb47/tensorflow_estimator_2.0_preview-1.14.0.dev2019091700-py2.py3-none-any.whl (450kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 45.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.1.0a0,>=2.0.0a0->tf-nightly-2.0-preview) (0.15.6)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.1.0a0,>=2.0.0a0->tf-nightly-2.0-preview) (41.2.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.1.0a0,>=2.0.0a0->tf-nightly-2.0-preview) (3.1.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tf-nightly-2.0-preview) (2.8.0)\n",
            "Installing collected packages: tb-nightly, tensorflow-estimator-2.0-preview, tf-nightly-2.0-preview\n",
            "Successfully installed tb-nightly-2.0.0a20190917 tensorflow-estimator-2.0-preview-1.14.0.dev2019091700 tf-nightly-2.0-preview-2.0.0.dev20190917\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOjujz601HcS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "269cd45e-418a-4196-9f2c-67661f904de3"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0-dev20190917\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zswl7jRtGzkk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "ab95dce0-6ddf-4776-98c1-1306031293d7"
      },
      "source": [
        "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
        "    plt.plot(time[start:end], series[start:end], format)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    plt.grid(False)\n",
        "\n",
        "def trend(time, slope=0):\n",
        "    return slope * time\n",
        "\n",
        "def seasonal_pattern(season_time):\n",
        "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
        "    return np.where(season_time < 0.1,\n",
        "                    np.cos(season_time * 6 * np.pi),\n",
        "                    2 / np.exp(9 * season_time))\n",
        "\n",
        "def seasonality(time, period, amplitude=1, phase=0):\n",
        "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
        "    season_time = ((time + phase) % period) / period\n",
        "    return amplitude * seasonal_pattern(season_time)\n",
        "\n",
        "def noise(time, noise_level=1, seed=None):\n",
        "    rnd = np.random.RandomState(seed)\n",
        "    return rnd.randn(len(time)) * noise_level\n",
        "\n",
        "time = np.arange(10 * 365 + 1, dtype=\"float32\")\n",
        "baseline = 10\n",
        "series = trend(time, 0.1)  \n",
        "baseline = 10\n",
        "amplitude = 40\n",
        "slope = 0.005\n",
        "noise_level = 3\n",
        "\n",
        "# Create the series\n",
        "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
        "# Update with noise\n",
        "series += noise(time, noise_level, seed=51)\n",
        "\n",
        "split_time = 3000\n",
        "time_train = time[:split_time]\n",
        "x_train = series[:split_time]\n",
        "time_valid = time[split_time:]\n",
        "x_valid = series[split_time:]\n",
        "\n",
        "window_size = 20\n",
        "batch_size = 32\n",
        "shuffle_buffer_size = 1000\n",
        "\n",
        "plot_series(time, series)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oeGTvnEXozmzqHOX++vy+8R8qPO4+Ds+QYzs2j/ssnepfqpi3PrHTqi3JOok+\n90729dmvLS/N/gZAagkA7WJGL8/ZmPLvX/T3Wbj5pdia2t3/XQIg3iEj2QLzFU9+jhdnb0BFVaxj\nr6zmuDXCZE8Hc6JSSbeiIXPtIhLquC4IJBWvp4uJaDWAdQA+ArAewPu1bZiI2hJRC+t1QwAjAaxA\nRGFcbu02FsA7tW0rGfs1+CG7YcQrhyLNK7vOzmtQl/jiLir1o/thcgbgPfnRWqggneP51yfhy0Cu\n1z09Mope9a8vUH5E3X14qJazloqqatz3zlLfjKc2hysjnfshl2mmUb2iQNfgu99eit73TI6+73vf\nZPS8O9bNvLUgNl50KhSbZ2ZFUn0772tnkN8Npx8dKDcQicj+09RVSffLR1LpnR4CcBKAVczcHcBw\nAGHk4e4AYAYRLQYwF8BUZn4XwK8A3E5EawC0BvBMCG0F8vcZMS8Lp3+96qUCp3LQOTWNjPBiDbpH\nYioC7uzFQPcgz6konv9sPS75x6dKvM1SPb1hFnOKHXN868P7tvfc/9s9mWcEraquwXtLtsQWXV0X\nNVPz6oGKKgx+cAp++fpivPD5BvxhylfYW17p6R3mVHTu9goLCPPvHZmRDG6+LjuI7fviz9UrczfC\n/cQ6390xqlfS3/3h8/6eT6mQTckIwyYVRVHJzDsBFBBRATPPAJCYOzdNmHkxMw9m5uOYuT8zP2ht\nX8vMQ5j5GGb+DjNrrdDhTO2gOugu3alxqG07mnavSTw9a13SkWOmuB+mB0fHe+Es+GYP3lmYWuI1\nFXyiINV63CEHdCbuwkLJ6HffZJzz2MeYs24X/jHza/z0xS/xwbJY+u4gm3mqrNy6D7vLKzFxUeSa\nbNp9CAMfnIKnPl4btwZQUVWNfvfFXF3D6jP9PNW8giN3HDgSNbUB8fd1/aJCtG2a6AavCvfCdl1X\nIancmXuIqAmATwC8SER/QSQ6OydxjoTW71QXS8HM2hewnTgfZK+I3T9OTb54V9t2iYBRxx6VsM8v\nXl+kpG1TpDreqKphn4Vab8qPVGPltv244snP8cqcSAe586D3uCqd33XiNolu3h1ZR5q2YltcGgvb\n5GQT1uj63x6puIHUzql7Ztwwxch0IZEg99jHiehUAKMBlCNS/nQyIi6sF+kRTz/uVL/Pf7peWVtB\n5oAZimMpnCkIvB6gXKynfe+F/Yy06zyXQd3nWY/OxLBHpqNk3KRo9tKDFVWYvHRr0ja+tQrZ+HWg\nmV7OFVviPeGdQW7On1zmyn3kXpcJm6c/SV5+1H3MOWwZUk7QjGIVgD8AWAZgPIABzDyBmf9qmaJy\nEndNhv2H1QWzBM0orntOXdqkSVmcAAAgAElEQVRtAHHmHd0PkN2e7joQe1OMOB/12EehtludRlGs\n7Zbv/3tWcrpfvbkYP/nPfKx25SBK1yyaqRl13FtL4t7btdbnrt8dV2Xtale6cr9B0OTbTstIjoTf\nWZZceX7jE5inClN6qEurhsl3qiVBcRR/YeZhiBQt2gngWSL6iojuI6LkK0N1iKuHhurtmxKM+HQW\nXlN12y4cdrsmcLar+4GyO8nX56eWVGDVtgOhLOjbv9C5ZSwHUMpK2dpvg5XDyDbHfbV1H5jZNxqX\nXf9/O6Y/ADXxIUGpZvyOs89RoYRgZYTq+y5ozVHlQreOSP9UUnhsYObfMfNgAFcBuAQRN9ac4RwP\nO3kUhRc4WZWv8e+pOc3uVrWm0oAh7xBKz04f5rNHlDh77N2+aeB3dh44goMVVdG03Ys27cXbCzbh\n3D9/gtfnb/I/hxxv5rLbNVVCM5sYM1h5kocoOm/xrFAURFRERBcR0YuIxE+sBHCpcsk0ErSmrPJ6\nJ1vLrs7Rh9s+7KCHqaKqutaj+g07D2L8+19FO/1LXB1Fr/b+ZVXsjjWsqOloFlfr6JN1Wm/M34SL\n/j4rrtbAz1+1ZhVb9vuaRN1nzG7XeSs9f92JylNfZ7p4HgZDHalxnPxseM+k1e5UobIf0VESIWgx\neyQRPYtIvqUfAZgEoAczX8nMyoPgdOK0pzZ3lSdVNTJI5dpu26fHM9jrGFXee6mc0973TMZDkzJP\nyvjWl5twxh9m4omPvsY6a93p3P7xM8fbRvhbUGuY8XXZAfS5dzLeSNFkFUQm99HasoPR9NlOdh2s\nwJl/mOn5Hfd1s9t1zijO7N0O5w3okL5AaeCMR9I9gzyxxFtREBG6t2mMo5NUvFNBp5bq1hF0qOSg\nGcVdAD4D0JeZL2bml5g5J91iA3O0KBwLZHOAzu6D6lKN2+fUPvpOLbwfohcdPvHpcvtrMRdbu5N0\np0k55Rj/0qC975kcXUCe4rFw+ub8TRj2yPSUZz21qQvh5r8Lv/X1SnOPLr1mFDro4lObQQdJz7GB\nx65vh2Z49ge1Dj/zREf6n6DF7LOZ+Wlm3q1cCsM41woS7PeG+/Klm/di2vJtyXcMmf0V6Xl73f32\nkmjxl2S400A/4/cApdi5vbv4W8xZtwtb9x5GybhJ+MJVwtOrHgUQmT0GpZWwFXn5keqExeM7Xl+E\nLXsPB7oROztnnSEzq7btx3ZrNlqYhWsUP04hnUZtMGn2svG63Ee3iZg62zTJvD6F133kLOuriuxL\n32kAExHSXnbFW88+JmHbhX+bhf97oXapBeLbTW2/T9fsTOuBe3H2N4HFX7zatfttP0+YVFO03fzS\nAlzx5OeYvS6iIOy8PzY1zCB4j7waO1KIuPnxv+cDiKSbH/jAlOj2l+d4R/86WbxpDy7752fR97E1\nCrUwgFGPfYyt+w6DiDxNT6YpUWz60VkYKh06t2yIs3q3xZPXZj6z+Of3TkjYdnRb/7W2sBBFgXgt\nPcS1EKZLhRCA20f11tNWitMkJZXu0piiZdq3TXXNwGynAC/f/kxmjHc5YgtufXkBfvh8LObl35+v\nR8m4SXHK6khVDfYrLG/rZMmm+MA3+5izYJAdRfUzdUVpl8DPOzaPmTrfuekUxdLEKCoswHPXDcEJ\n3Vpm/BuBHpoKEUWB+I7zfNcin7LFbDU/mxF+x5jMfVc1VTWcYEbKhLXWYrbX4aRzhM6ZhM2U5dvw\n4VexKPqHJkVcmp1pveNiSBSfUmeWVQCoSiPYTxW6azMksxA4q80VFYYjzM8DHCNyAVEUiO8Q3UXg\nVS44h/HTJeMm4brn5qT9vStKOyfdR7VJLpVze+VTYSQqrp0MNne5opS9sBXE+460G8xAn6Pi4yZ0\n5Pnaf7gS7y1JHsGsG7eDyOqHzwv594Np2bgejm4bMX+FVbSsY4sG6QnhwG3FOOWY1iFIFC6iKBB8\nszzuSEGercxYmX6FP6cfv3/wVqYShcs3O8tx/8Rl2LAz2Onu6+0HfD/bsvew57MbVnc96rGPfGMu\nmDmhFOa1w7rhsuOTK+vaMHf9bi0+9n40quedhK9fx/g1qTDLhF55YpeUlP+rNwzD89edGPjs28ok\nFbqm6eV11ZCYeWz0oI5xn110XEf37gCAhffFp2l/95ZT8ZAVea8aURRwuce6Pis/Uo2ScZPwvpV7\nJzQ0P793vrEI1z8/N7pA7LZlexHWAmhldQ1++uL8hO2pdtLTVmzD85+tj6uk5sVfP/RX6ocrq71N\nT2lqilWunEux7QeiJi43NRwzcdij6QbFhfjjFQPTazwDmjf097BRHXTnN2vq36l5tFjWcz84MdQ2\nmVO7r9o2rY8ze7dLnAlYfHLnWRjVz389YECn+OqMQ4+OnwUkc6t/eMyA6Ourh3TFJ3eeFZ3l+92T\nLRrFX8v+nZrj2pO6BbYTFqIokNr0U0WNhNrEaOwtr4wrN5mM1+ZtwvSvtmPCZxtAiC8OT/COUnar\nCWbGJ6vLfDtLP5Zs3ou56yNe1gcyqCb44LuRwLsdBzIPQPRTeemaFkc99rHvZ1v3eZdyrWY2tt5z\ni4cnnY3qoLugc9vMCmwN+7QwOK3fbNqgGOvHX5AQhNelVaPA33n7pycH/m4yGQoKCMX24IEIXVo1\nijpvEAjrx1+QVHadGFMURNSFiGYQ0XIiWkZEP7O2tyKiqUS02vqfuYtAisSl3Pe5wLqe82KfxbW9\n5ZVxQXADH5yCEx6amvbv+ymX139yMt67NT6z52X//Cxa0vLjVWXoftd7uPaZOYGdJQC8MucblIyb\nFDXFOM0fB9KMz/CTfV+aWX0jInh4PWUsTSLPf7bBp22ODUY064v6xebGggUGDjkyo0i/Ra/1OPeW\n8xyR/cmC3FKRYPFvzsGyB86JvncPZvo56svrMjH5YXJGUQXgDmbuh0ip1ZuIqB+AcQCmM3NPANOt\n90pJZbQX/sjH+/f7dWyesC8QUQyDXYrBWVMiXdzR6M0bFifYjtftOIi+90XqGE9zBdMdCWj7r9NX\nA4jNANyumSP6trMaTlNo63dmrNyO4+6fgtfmbsRr8zam+SOePxkKH6/yXiuqYf9Rbjp28EzQnco9\nru0UHhr7/L97y6k4o1fbWrdZw5k9q/ZX+ndqFs0J5v6dX5wTc19PdmypHHvDeoVxcTx2FutTekYy\nBowZHFurcJqYZv7iTEy7/fSkvx8mxhQFM29h5i+t1/sRyUjbCZFCSROs3SYAGKNallQuqooHzqvZ\n8ZcOSNyogHSPx71cMWfdrujrNa5FZHt0Zn+nJq5wD2XsaWLHInxgeRTd+eZi3PnG4pS/H9Tsk9cm\nBjKFRU2Nf8Dd2z9V68efzLnqqGbeNnrVbbs/6t+pOf7fpQNqXVsh08X7J649Adee1A0TbzoVj313\nkCVjvJTdW8cr9eeuC3d95fiuLbF+/AXRlDb9OngPGkvaNMYx7YKzD4dNVqxREFEJgMEAZgNoz8z2\nyvFWAN6V6EPEeUP7dqCaBmZ9O+jJ159OX324sjqhJKW90L1h50GM+NNH0e3//mIDNlnlMu19/DKa\nOs/1gntHptRJlIybhFfmpj+L8Dtee7NfvqkwqGZGgc+T5k5CGTbJBkFf/Ho4XvrR0Oj7V284SVvb\nbjq1aIgPbqvdSJmR2YyiR9smeGhM/zgTlFvRuc1TZ/Vu5/t7YXQXp1ozi9qk/AgL44rCqsf9JoDb\nmDmu7iJHhgeeQwQiuoGI5hHRvLKy9N1DnaQywt11INwo5UxHPskWsLfvO4xhj0zH2jJ/V1Eg/kZO\nNrs49XcfJmxjAGvLDiSYW/5mmZ0A4Iu1O7HzQAXeWRgfBOZl323ZuB4+vOPMQDlqi9dR2lGyKjts\n5xqF7kSQqTTXy1EbI8xkfkFNq3L6Y+bwZv8eJ69D89RmYGFd5o9+eSam3X5GOD9WC/wT3WiAiIoR\nURIvMvNb1uZtRNSBmbcQUQcAnsWjmfkpAE8BQGlpaa3uu1SyL34eQoSwGxVdxruLt2DL3sN44fMN\nuP/iYwEAv3KZZ4jSu5F3eCjJr7bsw9hnEwP97FKeAPCrN5cAiA9Sc7btliFMf3o3fnr5gdHH4gen\nlKCjwhlFxG5uZq0gHTNfo3qFoZyHl38UmZXYa2jpHHoqnfzRbRpj5LHt8eRHaxM+y3RG4S1LIpNu\nPQ1l+5N734WlrLq11p8S3QuTXk8E4BkAK5j5T46PJgIYa70eC0B57Qv3wq67wI1NTQ3jHzPX+Jah\n9OOLtTvx1+mr8a+PE2/sTFiz3d891T4WZsaijXuwatt+vJpkwTeTB+uR979K/0sWOtIie+HVWdcv\nKkSfo5opze5aw2zEAyjd9hoUewfIAUirhsOwHpGYgqDn5OcjeqJNk/o4vmu8U2Mq9+L3h3XDXef1\n9fzs9pG9QjvHY08uwZm9Iwvstptxq8b10NsRZX9iiY9TpjkfAiWYnFGcAuBaAEuIaKG17dcAxgN4\njYiuB7ABwBWqBXGPuvxu1pmrtuP3k1dizfYD+NMVg1L+fWcaih9ZKZYznQIxc2BaC1t0BjD68U99\n9xvetz0enbIqQykyhxBe2oQwUTnir6lhY8ccVrvXnVKCe99ZltZ32jWtHzfDdDK4a0vMu2dESr8z\npKQV6hUVYNaaHQCAa1xBZvdc0Be/tXJsdWvdOLQ0460a18Pz1wWn8H79JycbjX7XhUmvp1nMTMx8\nHDMPsv7eY+adzDycmXsy8whm3pX812qH82EKeqwWfrMHAFBeEU55zKCI8CAOVya6pn62Zgf2lB/B\n/f9LXhWusprjUg442554s/psmrpSbrsxpZ5q2BFT4CHE3LvjO8zvndQ1vMZDOmivrjCZh16HDMxY\n7vPz0Ohj8cL1Q3DZCZFZ/uhBHeNMlKf3aov/O+1oPP39Unx4R8SWr/2+CikrcTZjfDE7G0jwSPEZ\nINgpIsoD6ihv2HkQ5UeSRx9nOgg5VFntuaB99dOzccFfZ0Xf255HfviZf47r3CIzwdLAkOUp6cOr\nKgNodQ0HzljaNo1PRBnWALV/p9RMaqlejgsGdIiT9bsndkGrgMJPduxoOsfj3vfaYSVxJjGnrMse\nOAfPjI3UdhjRr320LkM2dNJZIEKoiKJA4vQ82X3tF1gFAGf8YSb63fdBSu0SEa47pSSlfW2GPDzd\n97PNe2LKwZn62rtt55u0RKgdRAizLGiYtGysxvOpgIJnFG4ysZw0b1iMs/vEu2sWEqVkUrMHDS0D\nCv4wA49fczzm/Hp4dBsR4diO/u7ctTXnOWNbvJRN4/pFng4Q2VBiOBtkCBNRFHCZnohCsTl+ncQ9\n1UZHumkvTK4TmKgoCCT3RFFVQvPc/h1SPt8/G94Tmaxg3TGqF351bp/4jUQpzShaNKqHhy/pjxeu\nH5p033Q6QOd6WarYHf+48/rEFemxkwheNNA7s6qgFlEUyMwUUjJuEqpctZ+dCubQkeB1DDuLa5HH\niOgnZ/RIX6A0MaUoDlZUZd1MwiYMRTGyX2J86FHNG3gGGXrx85G9MjI9XXtStzhvnEhbqbtpXjO0\nWzTo8M0bYwnvvj8sMTvpD04uwS+tdBZBssY88FISAUBkdrN+/AUJz8DRbZtg/fgLMLyv8vjbUMjS\nWzxjRFEgcYRr39fJonUnL4svCuN8IIIejuoaxuHKGhC8ZxTjzuuDRfeNCmy7tgRFo995rrqSrC/O\n3oCXZkcqxR30cAro1jq8gC83yRRUVQiK4vITvGtMpKMcM1EUfguqmShlr1KdzkHQ/Rcfi5vO8s9K\nG/TdfCFbB0OZIooCbtNTbHuyMolV1YxDR6rxxEdfY9W2/XFT7P0VlRj//lcoGTcp4XsvfL4eQCS5\nXZG1ku7uo5orLhAfZEIY2l1dhS3nqD3dDLCqsWVLd93ISU0N4/6L+iVsT2cG564DkmndiALKPK+W\nzehBEW+j0zJI2GffY/mnJswmY1SB0cjsbMHP9JTsIVu8aS9uezUSAvKHD1biq4fOjX525xuLfT2P\nJi6K1LZYs/1AVBmZrG2ceJjqHu3K6thve51fk4NPezYzsBaeX+VHqnFG73aAy03ZTpyYSk0N5ylY\ncO9IHKkOvjf8KuURMh/ZvvbjYVi34wBO6NYy5doIN591DE52lPGMrlHkoaaQGUUO4o6jiBUQCebZ\nT9dFX1fXcNxI0E9J/GPmGiyw4jGIYsVLqqqz52nS9WCbKubjxwUDOuC/N52SUJoyHWqYPe+b5Vsi\nacw+CvCYs3Gaflo2rpf0evz6/D6e24kyX4sa0r0VvntievEcvzinN07u0SaufSC2HifUXURRIED7\np/mM+ZXCdPL7ySvj3hdapqcw7OOZ4j5MXZJ4eT+p7FSS9ZlEhEFdWgSa5Z74XrAZqIAoGmtwVu+2\nmH5HfEK3VJTwlSd2iXvvPicTfhgfLeynDEiDAcTOJ+bXPoD8tD3lGGJ6QuLINtP7+ry/fJL2d+wZ\nRWUS80K+kO1mis4tgxfbiSL+/bUpZelWVO5z0qR+IY5q1gBb9x0GEDBryHAxOx2OadcEj1w6INBc\nl+WXNFSIrCp72TVZrjUyo4D/Yrbqax3xerJmFHloevLqQn4xSp3HVSZBUO7ayO1dhX66u5LleXkM\n1Rav4kJ2sjoAaFAv9hj//erB0deRNQr1PdZVQ7omVEcEMnOPretQ9H9uaQpRFEjU/rY7n+qHLKhE\npk4SR7Dqnuyrh3bFTWdFfOS90peP8cncGwbpnGo7W+rgri2x5P6Yq3LbpvUx754RmHb76RjQqTn+\ne1MkN1a7pvWxfvwFSdNCZ2JaKygg3HOBd7bUey7oi/pFsRQXFx7XEb+xvK5M31v5uEaRjQkvw0BM\nT/DoKK3/qgOIa9gZvZo9D5NKSc7u3Q4/tzzFpi7f5rnP2GHdMHX5Nny797BCSYJ5/7bTYDuiNaoX\n/5i0aVIfbZrUx/9uORVAxH31uC7qc2TZFBYURAstufNEAUA/q0qiW27d2KPqvJpR5KhylBlFAoQb\nz+iBVo3r4WIN6QKCpucDNXU+bn2osiwoUXJF9MDo/pj1q7Mx/Y4zamXrB+KL0qdD/aJCNKwXGakn\nGy+cN6CD0nPmZmDn5vj5yF6498J+uOi4xHv0xJJWuG1ET/z+8uO0yeRFrNPMH64ZGrnfivxq39ZR\ncutoQqJ/p+b48t6R+OmZqUefZkp01OXx2Vs3nuyxVT1dWjXChcd1CO33mjaIjWxTnZkXFBB6WNlA\n3bz0f8lzEtncc2HMZJNphxWmNeGMgMC1oPUN+xzeMbIXiAgNigtx/andPT3HCgoIt43ohTZNEmcb\nOvnFqN7o1roRju+qb7Zlmvsu7IeVvz3XWHEuVYjpyYWzU9CSvC4gc5qum82rI7x1eE+8u3hLKL//\n+8uOw6vzNmLmykgMgTvyOF1qOOItVpmCA0ABEW48swcKKPO1lzDWqn5yRg888dHXuNBjBgAAyx88\nJ3AUevkJXVBZzbiitIvvPtnGwC4t8NEvzzIthlYKCgj1C/wrBdZVRFEkoV5hQdLI2NqQbI3iodHH\nYva6XaF12qnSq33T5DuliFPhEajWNusa5pQT+BEQzaq6cVd57RquBb88pzeuGdrVtyZ1svWEwgLC\n9zI0owlCbTFqeiKiZ4loOxEtdWxrRURTiWi19T98f8M0UL0oFc2H49PMtcNK8OvzvT1eQpNBsSuf\ne2ZU23NawxxYs8EZqez0QqnNTOacY9vjuR+cmPH3CwsIXVqpS3goCCoxvUbxPIBzXdvGAZjOzD0B\nTLfea8PdZaoOmE6li65XZOYy/f7y43C3Q0nNdhStcRJkdwciRXUeuXQArhrSBaf2bBMdVRcnSbpo\nc+OZ8Smn/fr7Swd3wv0X9cMNp8f2d1qNanMtn7y2FGe5CgOp5MLjOiQ9r4KgC6OKgpk/BuCuiT0a\nwATr9QQAY7QK5eLOc9QFgAGpeYaYWpS8orQLfnT60dH37mAzm2YNgzPdlpa0QofmDfHIpcehuLAA\n914Y8fM/vmtqk8Ve7eMXtf1mBnee2wc/OKU7gFi1Nuf6Ql1Kd/33q49PSNUhpEc+LaKrJhvXKNoz\ns22Q3wrAs1IJEd0A4AYA6No1xGL0Ln58Rg98vLoMn67ZGfpvj+jbDgetAkdLN+8N/fdbNirG7vIU\nUnknGdjfdV4fDD3aP/V443qxxbvbR/bCn6auCvy9BlaA2KGA2uNOxgzqhJ7tmuKxqasw/avtqOHI\nLGXvoUoM79MORYWEJ68tjfvOf286BbPXxY9BDKbTEjSz4N6RUfdmofZko6KIwsxMRJ6PNzM/BeAp\nACgtLQ2tC9BZ6/bvVx+PcW8uBgBUVGVvrqcfO6qNNW9YjNZN6sUlQHQO1FM5e2t3RFJuL96UmnIk\nIvTv1Bw3ntkDn6zegRO6tcTEm0/BvPW7cZlPoaBurRt7REmLpsgXWjauZ1qEnML0GoUX24ioAwBY\n/7cblifaEb6Yhv9+KjQoLkzbBfdDVzbSFq4CRyP6tsftI3sBSH0EnY5uXPSbUfjwjjMx59fDMe32\nM3D5CZ3jKuKlsp5S7lHZLhVKS1ph1cPnoVXjeujWurGvkvBDZhSCkBnZqCgmAhhrvR4L4B2djXv1\nmbZNXMVcI93cMEc7gtBe/8kw/PuHQ3H10Jjp7emxpbjACparcfWM6x45vxaSxtOuWQMc064JHv3O\nQLR2rKH069gMJa0beZZ4tTGVDqe+IacA07x54zBMuvVU02IIdRijpiciehnAmQDaENEmAL8BMB7A\na0R0PYANAK4wJ6GLEDu4sVbR+lSL9/zmon54bd6m6PuLB3bEiSWtAAADOg+I1qEGYsrHOVtp06Se\nr1ktzH57QKfmmGkFWXmVgXXKp5tkCftylRO6tTItglDHMaoomPkqn4+8/TANYQd3hdnB2cFTqZqe\nrjulO66zPHqWP3hOXMZQN/ZPNm9YjN+O6Y9bXl7gWy4zLIoKCFU1jBaNYrbhx747EF9u2OMrnyAI\ndYOsXsw2gZcuOGKliqhXVICGxYUpe+u4uXRwJ7y1YDOAmL28MANrSLIoXjuAroYZFw3siOF920U9\njTz3D0EBTr7tNMxbvztu2yWDO+OSwYkKKtfy4AhCrpOfRts0sW399QoL8MVdw/G/m9O3915/ave4\n4i72ukeYsxQ7g6k7I22jekXK81Yd064prhySmptyQ0vRuRfiBUHITkRRuPBKZ/HXqwbjByeXoF+H\nZmjeqDhaA6CdRy0AJ87IWjvIzCZsRTH718Mx+bbTIr9ZYKcFyW43n7N764t0FgQhc0RRpED3No1x\n/8XHRjtgu5NPZkL5qSv1hBOOmp7CURTtmzVA0wZWNLK1LZk7qO2ZpNsQFFVgBixQ1wztaiwliiDU\nVWSNIgOSLW5Pvu00VNdwtNKYjXOAb3+mwl5vy+WVfK9143rYeTBSgrSoMLIArRu7RRN1hR++ZAAe\nvmSA9nYFoS4jQyuLPkdF0mqnktk0ajYqAGb+4sy4z753Ulf0OaoZju3Y3HeR+P8cBWeGlITvutiw\nOLJwPaBT84TP3vvZadHCP8VW/QMTykIQhLqDzCgs7GCsVPpMe5/iggKUtIn55v/5u4MwZnCnuH3b\nNKmPW4f7V8ob3jd8O33zRsX4702noGe7xApx7Zs1iCb3O/mY1vhg2bbA4DgVXDywI+as2xWtEyEI\nQnYjisKG4tcfgihp3QjXn9od1wyN9/LxKmU5754R0ddesxVVuaUGpVBv+y9XDsaGneVoXF/vbdCg\nuBCPfmeg1jYFQcgcURQWBVGX0uSKgojivJgW/WYUKiqr0c4nDXfi9zMSMXQaFBei91HhVbITBCE3\nEUVhURCdUaT/3eYNi4EkNRkEQRDqKrKYbWHPKNyJ9MIky8MaBEEQPBFFYUG1mFGkStQtNFtsT4Ig\nCCkgisIinTWK2iJqQhCEuoQoCovarFEIgiDkMqIoLAoLUnePFQRByCdEUbhQqSgaWcXedcctCIIg\n1Ias7bGI6FwAfwFQCOBpZh6vsr1ofiSFE4qrhnRF+ZFqXHdKibpGBEEQQiYrZxREVAjgcQDnAegH\n4Coi6hf8rdoRdY9VqCmKCwvwkzN6BFanEwRByDayUlEAGAJgDTOvZeYjAF4BMFplg7KYLQiC4E22\nKopOADY63m+ytinDXWtCEARBiJCtiiIpRHQDEc0jonllZWW1/z3rf7ZXhRMEQdBNtiqKzQC6ON53\ntrZFYeanmLmUmUvbtm2L2iKmJ0EQBG+yVVHMBdCTiLoTUT0AVwKYqLJBiaMQBEHwJisVBTNXAbgZ\nwAcAVgB4jZmXqWzzh6d2BwAM6R5+xTlBEIS6TNbGUTDzewDe09XeCd1aYv34C3Q1JwiCUGfIyhmF\nIAiCkD2IohAEQRACEUUhCIIgBCKKQhAEQQhEFIUgCIIQiCgKQRAEIRBRFIIgCEIgoigEQRCEQERR\nCIIgCIGIohAEQRACEUUhCIIgBCKKQhAEQQhEFIUgCIIQiCgKQRAEIRBRFIIgCEIgWVuPIp/43WUD\ncEy7pqbFEARB8EQURRbw3RO7mhZBEATBFyOmJyL6DhEtI6IaIip1fXYXEa0hopVEdI4J+QRBEIQY\npmYUSwFcCuBJ50Yi6gfgSgDHAugIYBoR9WLmav0iCoIgCIChGQUzr2DmlR4fjQbwCjNXMPM6AGsA\nDNErnSAIguAk27yeOgHY6Hi/ydqWABHdQETziGheWVmZFuEEQRDyEWWmJyKaBuAoj4/uZuZ3avv7\nzPwUgKcAoLS0lGv7e4IgCII3yhQFM4/I4GubAXRxvO9sbRMEQRAMkW2mp4kAriSi+kTUHUBPAHMM\nyyQIgpDXmHKPvYSINgEYBmASEX0AAMy8DMBrAJYDmAzgJvF4EgRBMAsx133zPhGVAdiQ4dfbANgR\nojiqEDnDoy7ICIicYVMX5NQtYzdmbptsp5xQFLWBiOYxc2nyPc0icoZHXZAREDnDpi7Ima0yZtsa\nhSAIgpBliKIQBEEQAhDpzCUAAAXDSURBVBFFYcVi1AFEzvCoCzICImfY1AU5s1LGvF+jEARBEIKR\nGYUgCIIQSF4rCiI610pnvoaIxhmWZT0RLSGihUQ0z9rWioimEtFq639LazsR0V8tuRcT0fEK5XqW\niLYT0VLHtrTlIqKx1v6riWisJjnvJ6LN1jldSETnOz7zTGev8p4goi5ENIOIlltp9n9mbc+q8xkg\nZ7adzwZENIeIFllyPmBt705Es602XyWietb2+tb7NdbnJcnkVyjj80S0znEuB1nbjT1DgTBzXv4B\nKATwNYCjAdQDsAhAP4PyrAfQxrXt9wDGWa/HAfid9fp8AO8DIAAnAZitUK7TARwPYGmmcgFoBWCt\n9b+l9bqlBjnvB/ALj337Wde7PoDu1n1QqPqeANABwPHW66YAVlmyZNX5DJAz284nAWhivS4GMNs6\nT68BuNLa/gSAG63XPwXwhPX6SgCvBsmvWMbnAVzusb+xZyjoL59nFEMArGHmtcx8BMAriKQ5zyZG\nA5hgvZ4AYIxj+wsc4QsALYiogwoBmPljALtqKdc5AKYy8y5m3g1gKoBzNcjph186e6X3BDNvYeYv\nrdf7AaxAJDtyVp3PADn9MHU+mZkPWG+LrT8GcDaAN6zt7vNpn+c3AAwnIgqQX6WMfhh7hoLIZ0WR\nckpzTTCAKUQ0n4husLa1Z+Yt1uutANpbr03Lnq5cJuW92ZrCP2ubdALk0SanZfYYjMgIM2vPp0tO\nIMvOJxEVEtFCANsR6Ty/BrCHmas82ozKY32+F0Br1XK6ZWRm+1w+bJ3Lx4iovltGlyxGn/l8VhTZ\nxqnMfDyA8wDcRESnOz/kyPwz61zUslUui38C6AFgEIAtAP5oVpwIRNQEwJsAbmPmfc7Psul8esiZ\ndeeTmauZeRAimaaHAOhjWKQE3DISUX8AdyEi64mImJN+ZVDEpOSzosiqlObMvNn6vx3A24jc9Nts\nk5L1f7u1u2nZ05XLiLzMvM16SGsA/Asxc4IxOYmoGJHO90VmfsvanHXn00vObDyfNsy8B8AMRBKN\ntiAiu4SCs82oPNbnzQHs1CWnQ8ZzLfMeM3MFgOeQRefSi3xWFHMB9LQ8JOohsrg10YQgRNSYiJra\nrwGMQqSu+EQAtnfDWAB2waeJAL5veUicBGCvw3Shg3Tl+gDAKCJqaZkrRlnblOJat7kEkXNqy+mV\nzl7pPWHZw58BsIKZ/+T4KKvOp5+cWXg+2xJRC+t1QwAjEVlPmQHgcms39/m0z/PlAD60ZnDKyhv4\nyPiVY2BAiKyhOM9l1jxDUXStmmfjHyIeBqsQsWvebVCOoxHxulgEYJktCyL20+kAVgOYBqCVtZ0A\nPG7JvQRAqULZXkbEzFCJiF30+kzkAvBDRBYJ1wC4TpOc/7bkWIzIA9jBsf/dlpwrAZyn454AcCoi\nZqXFABZaf+dn2/kMkDPbzudxABZY8iwFcJ/jeZpjnZvXAdS3tjew3q+xPj86mfwKZfzQOpdLAfwH\nMc8oY89Q0J9EZguCIAiB5LPpSRAEQUgBURSCIAhCIKIoBEEQhEBEUQiCIAiBiKIQBEEQAilKvosg\nCDZEZLuyAsBRAKoBlFnvy5n5ZCOCCYJCxD1WEDKEiO4HcICZHzUtiyCoRExPghASRHTA+n8mEX1E\nRO8Q0VoiGk9E11h1CZYQUQ9rv7ZE9CYRzbX+TjF7BILgjSgKQVDDQAA/AdAXwLUAejHzEABPA7jF\n2ucvAB5j5hMBXGZ9JghZh6xRCIIa5rKVf4uIvgYwxdq+BMBZ1usRAPpF0v0AAJoRUROO1S8QhKxA\nFIUgqKHC8brG8b4GseeuAMBJzHxYp2CCkC5iehIEc0xBzAwFsuomC0K2IYpCEMxxK4BSq8rZckTW\nNAQh6xD3WEEQBCEQmVEIgiAIgYiiEARBEAIRRSEIgiAEIopCEARBCEQUhSAIghCIKApBEAQhEFEU\ngiAIQiCiKARBEIRA/j/pk5AEEcAHLAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sTTIOCbyShY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
        "  dataset = dataset.batch(batch_size).prefetch(1)\n",
        "  return dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TW-vT7eLYAdb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1921a4af-3729-4d31-adca-7fdf2a8810e9"
      },
      "source": [
        "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
        "\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Dense(100, input_shape=[window_size], activation=\"relu\"), \n",
        "    tf.keras.layers.Dense(10, activation=\"relu\"), \n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-6, momentum=0.9))\n",
        "model.fit(dataset,epochs=100,verbose=0)\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fa19953ca90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efhco2rYyIFF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "44ea9295-e487-4056-8e2c-88bb8c52e4ac"
      },
      "source": [
        "forecast = []\n",
        "for time in range(len(series) - window_size):\n",
        "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
        "\n",
        "forecast = forecast[split_time-window_size:]\n",
        "results = np.array(forecast)[:, 0, 0]\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, results)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yMh0pxJSSTJgQQngms3dklGlZZFQSMgOVn3MCd8Nx0+B2+zBWO4tJveHLo77+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\n/2MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kT6j186YO6K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c61f35c4-4c74-4a3c-d619-b515544fcd0a"
      },
      "source": [
        "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0440106"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    }
  ]
}